The document discusses the global artificial intelligence in healthcare market. It predicts the market will be worth over $37 billion by 2029, growing at a CAGR of over 50% from 2019 to 2029. The increasing amounts of healthcare data and need for data management is driving adoption of AI. While deep learning and natural language processing show potential, a lack of skilled labor and unclear regulations may impede market growth. The market is segmented by region, technology, offering, end use, and end user.

1. Artificial Intelligence in Healthcare Market Global Trends, Market Share, Industry
Size, Growth, Opportunities, and Market Forecast 2019 to 2029
Global Artificial Intelligence in Healthcare Market is estimated to value over USD 37
billion by 2029 end and is expected to register a CAGR of over 50% during the
forecast period 2019 to 2029.
The artificial intelligence (AI) is capable of improving patient outcomes by accurately
identifying the source cause of the disease, this is positively influencing the market
growth. Furthermore, there has been a remarkable rise in venture capital
investments in AI technology which bolsters the market growth. Additionally, the
growing importance of big data in healthcare is expected to boost market growth.
The data generated in the healthcare industry is consistently mounting and to
efficiently manage this ever-increasing volume of big data, the adoption of artificial
intelligence becomes crucial and this is predicted to augment the market growth
significantly. For example, Google recently launched its healthcare project, Google
Deepmind Health, which can be efficiently used for collecting, storing and
normalizing healthcare database. These innovative projects that are supported or
initiated by tech giants will propel market growth.
The use of AI-based tools for elderly care has a high potential of showcasing
significant market growth during the forecast period. Moreover, markets like China
and India are unexplored and have untapped potential thus presenting lucrative
opportunities for the market to proliferate.
Nevertheless, a dearth of skilled labour for implementing AI technology and
imprecise regulatory scenario are some factors that can impede the market growth.
Insights on Global Artificial Intelligence in Healthcare:
Natural Language Processing Predicted to Prove Key to Effective Decision-making in
Healthcare Industry
Natural language processing (NLP) algorithm has a plethora of applications in
healthcare industry. It can enhance the wholeness and comprehensibility of
electronic health records by translating unbound text into standardised data. NLP
can fill semantic data lakes and data warehouses with relevant information that can
be accessed by free-text query user interfaces.
Additionally, it can manage documentation requirements very efficiently by
permitting providers with the authority of generating customised educational
materials for patients that are ready to be discharged. These factors are instrumental
in proliferating the growth of this segment.
Deep learning technology segment has untapped potential and is capable of
recording high market numbers during the forecast period. Currently, deep learning
is mainly used in small-scale research projects, before these projects enter the
commercialization stage. Nonetheless, deep learning is gradually finding its way into
newer and innovative tools that have applications of the highest value in the real-
world clinical environment.

2. Convolution neural networks (CNNs), a type of deep learning, is appropriate for
analysing images like X-rays or MRI results. CNNs are designed in a way that they can
efficiently process images thus permitting the networks to handle and operate larger
images. This has resulted into CNNs surpassing the accuracy of human diagnosticians
when it comes to identifying important imaging features in diagnostic studies.
The software segment dictates the market due to consistent innovations catering to
the requirement of healthcare sector. The hardware segment is projected to
showcase significant market growth during the forecast period.
Asia Pacific Region is a Lucrative Market and is Projected to Witness Remarkable
Growth During the Forecast Period
The Asia Pacific market has untapped potential owing to the massive investments in
research and development and rapid integration of AI technology in the healthcare
infrastructure of the region. Furthermore, an ever-increasing patient population shall
stimulate demand for better healthcare facilities and an improved healthcare
infrastructure which will further bolster the growth of the market.
Drug Discovery Application Shall Showcase Noteworthy Market Growth
Drug discovery application is anticipated to exhibit remarkable market growth during
the forecast period. The integration of AI technology in drug discovery will help
lessen the production costs required for developing new drugs.
Recently, Berg, a Boston-based AI specialist company, collaborated with Astra Zeneca
for drug discovery. Such partnerships shall positively influence the proliferation of
the market.
Key Market Players:
• IBM (Watson Health)
• AiCure
• APIXIO Inc.
• iCarbonX
• Insilico Medicine Inc.
• Sophia Genetics
• Welltok
• Zebra Medical Vision Ltd.
IBM Watson is an ideal example of a machine-learning NLP technology in the
healthcare industry. It has a huge appetite for gathering academic literature and it is
gaining expertise in clinical decision support (CDS) for cancer care and precision
medicine. In 2014, IBM partnered with EHR developer Epic and the Carillion clinic
located in Virginia to investigate how machine learning and NLP could be utilised for
warning patients suffering from heart disease. this collaboration took place even
before IBM set up a dedicated Watson health division.
The system not only highlighted pertinent clinical data but also identified behavioural
and social factors that are recorded in the clinical note but somehow don’t make it
into the structured EHR template. Like for instance, the system checks the mental
status of the patient by verifying whether the patient is depressed or not. Using its

3. machine learning capabilities, the Watson system also identifies the living status of
the patient like checking the financial status of the patient. Watson acquired a
database of around 21 million records in just six weeks and achieved an accuracy
rate of close to 85% for patient identification which is remarkable considering the
short period it took for processing such a huge volume of database.
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Global Artificial Intelligence in Healthcare Market Segmentation:
1. By Region
• North America
• Europe
• Asia Pacific
• Latin America
• Rest of the World
2. By Technology
• Natural Language Processing (NLP)
• Machine Learning
o Deep Learning
o Supervised Learning
o Unsupervised Learning
o Reinforcement Learning
• Context-Aware Computing
• Computer Vision
3. By Offering
• Software
• Hardware
o Processor
o Memory
• Services
o Deployment & Integration
o Support & Maintenance
4. By End-Use Application
• In-Patient Care & Hospital
• Patient Data and Risk Analysis
• Medical Imaging & Diagnostics
• Lifestyle Management & Monitoring
• Virtual Assistant
• Healthcare Assistant Robots
• Wearable
• Mental Health

4. 5. By End User
• Patients
• Hospitals
• Biotechnology Companies
• High-tech Hospitals and Research Labs
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Competitive Landscape:
• Tier 1 players- established companies in the market with a major market share
• Tier 2 players
• Emerging players which are growing rapidly
• New Entrants
FutureWise Key Takeaways:
• With an ever-increasing patient population, the healthcare database volume shall
be consistently increasing thus making the AI technology more crucial for database
management
• Deep learning technology is currently at its nascent stage and further
advancements in this technology shall expand the market significantly
Objectives of the Study:
• To provide with an exhaustive analysis on the artificial intelligence in healthcare
market by region, by technology, by offering, by end-use application, by end user
• To cater comprehensive information on factors impacting market growth (drivers,
restraints, opportunities, and industry-specific restraints)
• To evaluate and forecast micro-markets and the overall market
• To predict the market size, in key regions (along with countries)— North America,
Europe, Asia Pacific, Latin America and rest of the world
• To record evaluate and competitive landscape mapping- product launches,
technological advancements, mergers and expansions
• Profiling of companies to evaluate their market shares, strategies, financials and
core competencies
Table of Contents
1. Introduction
1.1. Scope and Objective
1.2. Assumptions and Acronyms
1.3. Forecast Factors
1.4. Research Methodology
2. Executive Summary
2.1. Industry Cluster Analysis

5. 2.2. Competition Matrix
2.3. Strategies Recommendations
3. Market Definition
3.1. Report Scope (Inclusions & Exclusions)
3.2. Market Segmentation
4. Global Artificial Intelligence in Healthcare Market Overview
4.1. Global Artificial Intelligence in Healthcare Market Revenue (USD Mn)
5. Key Inclusions
5.1. Porter’s Five Force analysis
5.2. Market Dynamics
5.3. Industry Trends
5.4. Regulatory Guidelines
5.5. Opportunities for Artificial Intelligence in Healthcare Market
6. Competition Dynamics
6.1. Company Share Analysis (2019)
7. Global Artificial Intelligence in Healthcare Market Revenue (USD Mn), 2019-2029
by Technology
7.1. Key Market Findings
7.2. Machine Learning
7.2.1 Deep Learning
7.2.2 Supervised Learning
7.2.3 Reinforcement Learning
7.2.4 Unsupervised Learning
7.3. Natural Language Processing
7.4. Context-Aware Computing
7.5. Computer Vision
8. Global Artificial Intelligence in Healthcare Market Market Revenue (USD Mn),
2019-2029 by End Use Application
8.1. Key Market Findings
8.2. Inpatient Care & Hospital
8.3. Patient Data and Risk Analysis
8.4. Medical Imaging & Diagnostics
8.5. Lifestyle Management & Monitoring
8.6. Virtual Assistant
8.7. Healthcare Assistance Robots
8.8. Wearable
8.9. Mental Health
9. Global Artificial Intelligence in Healthcare Market Revenue (USD Mn), 2019-2029
by Offering
9.1. Key Market Findings
9.2. Hardware (Processor, Memory)
9.3. Computing Architecture (Network)

6. 9.4. Software
9.5. Solutions (Cloud, AI Platform, Own ML Frameworks)
10. Global Artificial Intelligence in Healthcare Market Revenue (USD Mn), 2019-
2029 by End User
10.1. Key Market Findings
10.2. Patients
10.3. Hospitals and Providers
10.4. Biotechnology Companies
10.5. High tech Hospitals and Research labs (Healthcare Assistance Robots)
11. Global Artificial Intelligence in Healthcare Market Revenue (USD Mn), 2019-
2029 by Region
11.1. Key Market Findings
11.2. Long Term ROI Segments
11.3. Revenue Opportunity Influencing Factors
11.4. Market Revenue (USD Mn) Assessment and Forecast by Region,2029
11.4.1 North America
11.4.2 Latin America
11.4.3 Europe
11.4.4 Asia Pacific
11.4.5 Rest of world
12. North America Artificial Intelligence in Healthcare Market Revenue (US$ Mn),
2019-2029
12.1. Key Market Findings
12.2. Long Term ROI Segments
12.3. Revenue Opportunity Influencing Factors
12.4. Market Revenue (USD Mn) Assessment and Forecast by Country, 2019-2029
12.4.1 US
12.4.2 Canada
13. Latin America Artificial Intelligence in Healthcare Market Revenue (US$ Mn),
2019-2029
13.1. Key Market Findings
13.2. Long Term ROI Segments
13.3. Revenue Opportunity Influencing Factors
13.4. Market Revenue (USD Mn) Assessment and Forecast by Country, 2019-2029
13.4.1 Brazil
13.4.2 Mexico
13.4.3 Argentina
13.4.4 Rest of Latin America
14. Europe America Artificial Intelligence in Healthcare Market Revenue (US$ Mn),
2019-2029
14.1. Key Market Findings
14.2. Long Term ROI Segments

7. 14.3. Revenue Opportunity Influencing Factors
14.4. Market Revenue (USD Mn) Assessment and Forecast by Country, 2019-2029
14.4.1 Germany
14.4.2 France
14.4.3 Spain
14.4.4 UK
14.4.5 Russia
14.4.6 Poland
14.4.7 Rest of Europe
15. Asia Pacific Artificial Intelligence in Healthcare Market Revenue (US$ Mn), 2019-
2029
15.1. Key Market Findings
15.2. Long Term ROI Segments
15.3. Revenue Opportunity Influencing Factors
15.4. Market Revenue (USD Mn) Assessment and Forecast by Country, 2019-2029
15.4.1 Emerging Asia
15.4.1.1 China
15.4.1.2 India
15.4.1.3 ASEAN-5
15.4.1.4 Rest of Emerging Asia
15.4.2 Japan
16. Rest of World Artificial Intelligence in Healthcare Market Revenue (US$ Mn),
2019-2029
16.1. Key Market Findings
16.2. Long Term ROI Segments
16.3. Revenue Opportunity Influencing Factors
16.4. Market Revenue (USD Mn) Assessment and Forecast by Country, 2019-2029
16.4.1 Middle East
16.4.2 South Africa
16.4.3 North Africa
16.4.4 Rest of World
16.4.5 Others
17. Company Profiles
17.1. Competition Landscape
17.2. Global Company Share (USD Mn) Overview, 2019
17.3. Company Profiles
17.3.1 Nvidia
17.3.1.1 Corporate Overview
17.3.1.2 Financial Performance
17.3.1.3 Peer Comparison & 3C marketing equation
17.3.1.4 Company Strategy & Channel Management
17.3.2 Intel
17.3.2.1 Corporate Overview

8. 17.3.2.2 Financial Performance
17.3.2.3 Peer Comparison & 3C marketing equation
17.3.2.4 Company Strategy & Channel Management
17.3.3 Google
17.3.3.1 Corporate Overview
17.3.3.2 Financial Performance
17.3.3.3 Peer Comparison & 3C marketing equation
17.3.3.4 Company Strategy & Channel Management
17.3.4 IBM
17.3.4.1 Corporate Overview
17.3.4.2 Financial Performance
17.3.4.3 Peer Comparison & 3C marketing equation
17.3.4.4 Company Strategy & Channel Management
17.3.5 Microsoft
17.3.5.1 Corporate Overview
17.3.5.2 Financial Performance
17.3.5.3 Peer Comparison & 3C marketing equation
17.3.5.4 Company Strategy & Channel Management
17.3.6 Medtronic
17.3.6.1 Corporate Overview
17.3.6.2 Financial Performance
17.3.6.3 Peer Comparison & 3C marketing equation
17.3.6.4 Company Strategy & Channel Management
17.3.7 General Electric
17.3.7.1 Corporate Overview
17.3.7.2 Financial Performance
17.3.7.3 Peer Comparison & 3C marketing equation
17.3.7.4 Company Strategy & Channel Management
17.3.8 Amazon Web Services
17.3.8.1 Corporate Overview
17.3.8.2 Financial Performance
17.3.8.3 Peer Comparison & 3C marketing equation
17.3.8.4 Company Strategy & Channel Management
17.3.9 Micron Technology
17.3.9.1 Corporate Overview
17.3.9.2 Financial Performance
17.3.9.3 Peer Comparison & 3C marketing equation
17.3.9.4 Company Strategy & Channel Management
17.3.10 Pillo
17.3.10.1 Corporate Overview
17.3.10.2 Financial Performance
17.3.10.3 Peer Comparison & 3C marketing equation
17.3.10.4 Company Strategy & Channel Management

9. 17.3.11 Catalia Health
17.3.11.1 Corporate Overview
17.3.11.2 Financial Performance
17.3.11.3 Peer Comparison & 3C marketing equation
17.3.11.4 Company Strategy & Channel Management
17.3.12 Ginger.Io
17.3.12.1 Corporate Overview
17.3.12.2 Financial Performance
17.3.12.3 Peer Comparison & 3C marketing equation
17.3.12.4 Company Strategy & Channel Management
17.3.13 BioBeats
17.3.13.1 Corporate Overview
17.3.13.2 Financial Performance
17.3.13.3 Peer Comparison & 3C marketing equation
17.3.13.4 Company Strategy & Channel Management
17.3.14 Icarbonx
17.3.14.1 Corporate Overview
17.3.14.2 Financial Performance
17.3.14.3 Peer Comparison & 3C marketing equation
17.3.14.4 Company Strategy & Channel Management
17.3.15 Qventus
17.3.15.1 Corporate Overview
17.3.15.2 Financial Performance
17.3.15.3 Peer Comparison & 3C marketing equation
17.3.15.4 Company Strategy & Channel Management
17.3.16 Caresyntax
17.3.16.1 Corporate Overview
17.3.16.2 Financial Performance
17.3.16.3 Peer Comparison & 3C marketing equation
17.3.16.4 Company Strategy & Channel Management
17.3.17 Gauss Surgical
17.3.17.1 Corporate Overview
17.3.17.2 Financial Performance
17.3.17.3 Peer Comparison & 3C marketing equation
17.3.17.4 Company Strategy & Channel Management
17.3.18 Perceive3d
17.3.18.1 Corporate Overview
17.3.18.2 Financial Performance
17.3.18.3 Peer Comparison & 3C marketing equation
17.3.18.4 Company Strategy & Channel Management
17.3.19 Next IT (A Verint Systems Company)
17.3.19.1 Corporate Overview
17.3.19.2 Financial Performance

10. 17.3.19.3 Peer Comparison & 3C marketing equation
17.3.19.4 Company Strategy & Channel Management
17.3.20 Atomwise
17.3.20.1 Corporate Overview
17.3.20.2 Financial Performance
17.3.20.3 Peer Comparison & 3C marketing equation
17.3.20.4 Company Strategy & Channel Management
18. Research Sources & Primary Verbatim
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